There is a need for a computerized system configured to plan a grasp approach, position, and pre-grasp pose for implementation by a robotic hand. The system would facilitate grasping of three-dimensional objects (e.g., complex three-dimensional objects) in a speedy, feasible, and reliable manner.
Such a computerized solution does not exist, and manual training efforts toward the goal are too tedious and consumptive in terms of time and processing resources for use in real-time applications such as on a manufacturing assembly line.
One iterative manual method for arranging a grasp position and a grasp configuration consists of (1) manually moving the robot hand to near the object, (2) positioning the fingers around the object for a desired grasp, (3) using a command program to initiate closure of the fingers around the object, (4) checking to see if the object is securely held by manually attempting to shaking the object out of the robot grasp, (5) making a mental judgment of whether other robot finger positions would be better, and, if so, (6) repeating above steps until the operator is satisfied with the grasp.
Some conventional systems generate an approach based simply on feedback from real-time vision systems and/or touch sensors. Some conventional methods, such as attempting to grasp an object based only on feedback from a camera and/or one or more on-robot touch sensors, require human, manual, interaction to control or teach the robot hand. All are too computationally expensive and time consuming for performing efficiently in real-time applications.